cusum.obs_minus_exp: Creats a cumulative observed minus expected failure plot

In meyera/rcusum: Tools for the easy creation cusum analysis charts

Description

Implementation of an unadjusted or risk-adjusted cumulative observed minus expected failure graph as described in Rogers et al. (2004)

Usage

  cusum.obs_minus_exp(failure_indicator, p0, by = NULL,
    scale_ylim = 20)

Arguments

failure_indicator	a numeric indicator variable consiting of only c(0,1), where 0 is no failure and 1 is failure for each procedure
p0	either a constant which represents the acceptable event rate, or a numeric vector representing the acceptable risk score for each single individual. The later is used when plotting risk audjusted scores. This is then equal to an VLAD (variable life adjusted displays) or CRAM (cumulative risk adjusted mortality) chart
by	a factor vector consisting of the stratification variable.
scale_ylim	Limits the Y axis scale

Details

"The graph starts at 0, but is incremented by 1 - p0 for a failure and decremented by p0 for a success"

Value

an object of the class ggplot

Author(s)

Alexander Meyer

References

Rogers, C. A., Reeves, B. C., Caputo, M., Ganesh, J. S., Bonser, R. S., & Angelini, G. D. (2004). Control chart methods for monitoring cardiac surgical performance and their interpretation Chris. The Journal of Thoracic and Cardiovascular Surgery, 128(6), 811–819. doi:10.1016/j.jtcvs.2004.03.011

See Also

Other cusum: cusum, cusum.sprt

Examples

set.seed(16)
df = data.frame(
 is_failure = c(rbinom(50,1,0.10),rbinom(50,1,0.08),rbinom(50,1,0.05),
                 rbinom(50,1,0.10),rbinom(50,1,0.13),rbinom(50,1,0.14),
                 rbinom(50,1,0.14),rbinom(50,1,0.09),rbinom(50,1,0.25)
 ),
 p0 = c(rnorm(50, 0.10, 0.03),rnorm(50, 0.10, 0.03),rnorm(50, 0.10, 0.03),
         rnorm(50, 0.10, 0.03),rnorm(50, 0.10, 0.03),rnorm(50, 0.10, 0.03),
         rnorm(50, 0.10, 0.03),rnorm(50, 0.15, 0.03),rnorm(50, 0.20, 0.03)
 ),
 by=rep(factor(c("Surgeon A", "Surgeon B", "Surgeon C")), times=c(150,150,150))
)
cp= cusum.obs_minus_exp(rbinom(200,1,0.10), c(rnorm(100, 0.10, 0.03),rnorm(100, 0.10, 0.03)))
print(cp)
cp= cusum.obs_minus_exp(rbinom(200,1,0.10), 0.10)
print(cp)
cp= cusum.obs_minus_exp(df$is_failure, 0.10, by=df$by)
print(cp)
cp= cusum.obs_minus_exp(df$is_failure, df$p0, by=df$by)
print(cp)

meyera/rcusum documentation built on May 22, 2019, 7:54 p.m.